@inproceedings{ta-etal-2026-faid,
    title = "{FAID}: Fine-grained {AI}-generated Text Detection using Multi-task Auxiliary and Multi-level Contrastive Learning",
    author = "Ta, Minh Ngoc  and
      Van, Dong Cao  and
      Hoang, Duc-Anh  and
      Le-Anh, Minh  and
      Nguyen, Truong  and
      Nguyen, My Anh Tran  and
      Wang, Yuxia  and
      Nakov, Preslav  and
      Sang, Dinh Viet",
    editor = "Demberg, Vera  and
      Inui, Kentaro  and
      Marquez, Llu{\'i}s",
    booktitle = "Proceedings of the 19th Conference of the {E}uropean Chapter of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
    month = mar,
    year = "2026",
    address = "Rabat, Morocco",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-eacl/2026.eacl-long.151/",
    pages = "3275--3296",
    ISBN = "979-8-89176-380-7",
    abstract = "The growing collaboration between humans and AI models in generative tasks has introduced new challenges in distinguishing between *human-written*, *LLM-generated*, and *human-LLM collaborative* texts. In this work, we collect a multilingual, multi-domain, multi-generator dataset *FAIDSet*. We further introduce a fine-grained detection framework *FAID* to classify text into these three categories, and also to identify the underlying LLM family of the generator. Unlike existing binary classifiers, FAID is built to capture both authorship and model-specific characteristics. Our method combines multi-level contrastive learning with multi-task auxiliary classification to learn subtle stylistic cues. By modeling LLM families as distinct stylistic entities, we incorporate an adaptation to address distributional shifts without retraining for unseen data. Our experimental results demonstrate that FAID outperforms several baselines, particularly enhancing the generalization accuracy on unseen domains and new LLMs, thus offering a potential solution for improving transparency and accountability in AI-assisted writing. Our data and code are available at [https://github.com/mbzuai-nlp/FAID](https://github.com/mbzuai-nlp/FAID)"
}